230 research outputs found
Instantons in Large Order of the Perturbative Series
Behavior of the Euclidean path integral at large orders of the perturbation
series is studied. When the model allows tunneling, the path-integral
functional in the zero instanton sector is known to be dominated by bounce-like
configurations at large order of the perturbative series, which causes
non-convergence of the series. We find that in addition to this bounce the
perturbative functional has a subleading peak at the instanton and
anti-instanton pair, and its sum reproduces the non-perturbative valley.Comment: 9 pages (without figures), KUCP-6
Enhanced news sentiment analysis using deep learning methods
We explore the predictive power of historical news sentiments based on financial market performance to forecast financial news sentiments. We define news sentiments based on stock price returns averaged over one minute right after a news article has been released. If the stock price exhibits positive (negative) return, we classify the news article released just prior to the observed stock return as positive (negative). We use Wikipedia and Gigaword five corpus articles from 2014 and we apply the global vectors for word representation method to this corpus to create word vectors to use as inputs into the deep learning TensorFlow network. We analyze high-frequency (intraday) Thompson Reuters News Archive as well as the high-frequency price tick history of the Dow Jones Industrial Average (DJIA 30) Index individual stocks for the period between 1/1/2003 and 12/30/2013. We apply a combination of deep learning methodologies of recurrent neural network with long short-term memory units to train the Thompson Reuters News Archive Data from 2003 to 2012, and we test the forecasting power of our method on 2013 News Archive data. We find that the forecasting accuracy of our methodology improves when we switch from random selection of positive and negative news to selecting the news with highest positive scores as positive news and news with highest negative scores as negative news to create our training data set.Published versio
Complex Correlation Approach for High Frequency Financial Data
We propose a novel approach that allows to calculate Hilbert transform based
complex correlation for unevenly spaced data. This method is especially
suitable for high frequency trading data, which are of a particular interest in
finance. Its most important feature is the ability to take into account
lead-lag relations on different scales, without knowing them in advance. We
also present results obtained with this approach while working on Tokyo Stock
Exchange intraday quotations. We show that individual sectors and subsectors
tend to form important market components which may follow each other with small
but significant delays. These components may be recognized by analysing
eigenvectors of complex correlation matrix for Nikkei 225 stocks.
Interestingly, sectorial components are also found in eigenvectors
corresponding to the bulk eigenvalues, traditionally treated as noise
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